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• optimizations ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64e0c6c5-19c7-4898-9605-049f7c31342e
• In this work, to significantly reduce the manual effort during meta-model and grammar co-evolution, we present an automated approach for extracting optimization rule configurations. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64e0c6e0-efbf-478d-b647-2d0eac1f9751
• replay optimizations on later versions of the grammar, thus leading to a fully automated migration process for the supported types of changes. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64e0c6f1-8136-4aee-bcb2-6d87fd24bab7
• We consider a scenario in which a meta-model is coevolved with an associated grammar. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64e0c96e-a290-48f0-8aed-1d4633d1dc21
• First, the meta-model evolves over time, rendering previous versions of the grammar obsolete. Second, in a rapid prototyping context, the meta-model evolves quickly and then requires the grammar to be updated quickly as well. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 64e0c98c-59d3-4cca-8338-1e23ecd3b2bb
• The updated grammar should be consistent with the new version of the meta-model. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64e0cb41-ce79-4e10-a35a-4331ce1a2e05
• The updated grammar should incorporate any manual improvements that were made to previous versions of the grammar ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 64e0cb46-b5d9-4c20-a89e-4566425f0fce
• we present an approach for automating the configuration of grammar optimization rules. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64e0cc4e-af1e-4765-9235-64074a2c764c
• Our approach can then automatically extract an optimization rule configuration that encodes the manual improvements. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 64e0cc5c-ef11-4f62-bb02-53b59f03aa81
• mapping between the grammar rules from both grammars and then, per rule, performing a line-by-line comparison to extract invocations of relevant grammar optimization rules with their parametrizations. ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 64e0cc69-2df9-4751-85c8-9f41fdb7bdb0
• The meta-model represents the abstract syntax for language at hand (classes with their features, including names, attributes, and references), while the augmented EBNF expression describes the concrete syntax and its mapping to specific parts of the meta-model. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 64e0cd27-77f3-41d9-94ba-b66db2993508
• This AST can then be further processed or used for various purposes in language development ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e0cdd3-9ac3-4d9a-9acf-afae8a3fd58c
• Their approach includes 54 optimization rules extracted from seven sample languages, which are used to optimize the generated grammar (explained above). ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e0ce58-db86-488e-a4c5-a2086a27c854
• AddKeywordToAttr ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1d8ab-6f9b-42ea-8dd4-6e5c616fde40
• ChangeBracesToSquare ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1d8b1-836e-41f5-b936-316ffeed2bc7
• GrammarOptimizer ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1d974-15dc-4bdd-a228-340430802c51
• the names of the grammar rule, of the attribute name, of the current type, and of the new type ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1d9d1-52f8-44c7-8f8f-b91f1eceed26
• GrammarOptimizer ls-type:: annotation hl-page:: 3 hl-color:: blue id:: 64e1d9d4-f8b3-4420-b23f-76b4a9a96290
• go.changeTypeOfAttr("SiteWithModal", "name","EString", "ID") ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1d9de-a271-4fb0-b82a-f8f14e39ea60
• Recovery of grammars and meta-models ls-type:: annotation hl-page:: 3 hl-color:: blue id:: 64e1da20-8965-4b39-8a79-7fca64d9cafe
• Co-evolution in MDE contexts ls-type:: annotation hl-page:: 3 hl-color:: blue id:: 64e1da25-cc3d-4fbb-9e96-9dca9180c573
• Automated rule extraction ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 64e1da2b-319e-47a8-a020-e409e8b06e20
• From meta-models to graph grammars ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 64e1da2f-4252-419f-bc9a-95ba6f5cd81f
• Text-based merging ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 64e1da34-f23c-458a-a7eb-6f8b7532e031
• Grammar convergence ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 64e1da38-b71a-4cce-b90d-5624879bdb3a
• However, except for GrammarOptimizer [ 47] (described in Sect. 2), on which we build and improve with our contribution, we are not aware of previous work on meta-model/grammar co-evolution ls-type:: annotation hl-page:: 3 hl-color:: green id:: 64e1dc50-d4e5-49cd-94e3-e51db48dbe01
• A line of work focuses on automating the extraction of transformation rules in specific contexts. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e1dc59-d63d-4f22-b499-b2762c903e0a
• we focus on the automated extraction of configurations of rules. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e1dc9b-d7ec-436a-ac0b-67090649cc46 hl-stamp:: 1692523677445
• double-pushout approach to graph rewriting, using advanced transformation features such as negative application conditions. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e1dcca-37ef-4455-808b-7696042f6495
• avoid the use of such advanced features that make analysis more complicated, while being sufficient for meta-models with arbitrary multiplicities and inheritance ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e1dcd7-05aa-4f33-b90b-93ecab7ba145
• Grammar convergence aims to extract a series of transformations to make two considered grammars syntactically identical, which is similar to our goal. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e1dd53-aec6-4c28-bbb8-4b6b7576fe54
• We decided to directly work with a subset of six of their considered languages–Bibtex, DOT, EAST-ADL (full version), SML, Xcore, and Xenia–, which has the following benefits: ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1dddc-7e7e-4bc8-a57c-ddf2c0936178
• that four of their considered languages had complications that led to a lack of full support (e.g., using OCL as part of the grammar definition). ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1de5d-ab7d-4158-91b2-641011a88972
• enerated grammar (i.e., the grammar newly generated from the meta-model). ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1de84-44bb-49ca-88b6-8700e5e14a3c
• We observed that EAST-ADL and Bibtex did not have original grammars in Xtext, so we directly adopted the optimized grammars from [ 47 ] ls-type:: annotation hl-page:: 5 hl-color:: red id:: 64e1de9a-17be-4d49-9469-77fbdb62cb0c
• We developed the initial version of ConfigGenerator based on EASTADL ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1df18-de10-46c9-88b1-1ff182010d26
• Each time we implemented a comparison method for a specific grammar element (e.g., comparing line orders), we applied it to compare two EAST-ADL grammars and check the selected optimization rules. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1df28-d6d1-4b9c-81bd-8f3107355f7b
• in Xenia’s target grammar, some different attributes are placed on the same line ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1df86-b753-4ace-9356-2215f636a114
• ConfigGenerator ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1dfa3-526d-403d-b17b-0b94698219b2
• extracting the optimization rule configurations. ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 64e1e050-7e5d-4ef9-ba77-12891b81ef0a
• optimized grammar and the target grammar of each language, based on a one-to-one comparison of corresponding grammar rules. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 64e1e0be-512a-4939-afe9-fbe3c1f052e7
• The 2nd column shows the number of lines of optimization rule configurations used by Zhang ls-type:: annotation hl-page:: 5 hl-color:: red id:: 64e1e1e4-44c6-4114-87aa-c5fdf8dc0407
• “Diff” represents grammar rules that are not identical, and "Percent” indicates the percentage of grammar rules that are identical between the two grammars ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e1e25b-ac30-4764-88f5-31b416bb09bf
• In this section we present the ConfigGenerator, which creates an optimization rule configuration based on a generated grammar and a target grammar, to enable a re-application of manually defined grammar changes after a meta-model changed and a new grammar was generated ls-type:: annotation hl-page:: 6 hl-color:: purple id:: 64e1e285-1cd9-4296-8678-6f3f85d54229
• A grammar rule name is unique across the grammar. ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e1e2ed-f523-4a16-8239-b399981966ee
• An attribute name is unique within a grammar rule ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e1e486-fa7e-4a00-9470-ec45ccb78907
• Attribute names are not modified by users when they manually create a target grammar out of a generated grammar. ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e1e498-ee8a-4869-a30c-5615935929c2
• Figure 3 illustrates the internal workflow of ConfigGenerator for selecting the required optimization rules by comparing two grammars. ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e1e4dc-38e1-484c-af23-aa458f289928
• If no match is found, it indicates that the grammar rule has been deleted in the target grammar, thereby requiring the selection and parameterization of an optimization rule for deleting that grammar rule ls-type:: annotation hl-page:: 6 hl-color:: green id:: 64e24686-5416-4ba2-95ff-112bcb7cb9a4
• After both traversals are completed, ConfigGenerator yields an optimization rule configuration with the selected and parameterized optimization rules and writes it into a text file. ls-type:: annotation hl-page:: 7 hl-color:: green id:: 64e24776-03ab-4463-a85f-82b7eba05dd9
• n optimization rule for adding the grammar rule is selected and parameterize ls-type:: annotation hl-page:: 7 hl-color:: green id:: 64e2477c-a245-4776-b190-58365103b087
• sing regular expressions, we separate different attributes into distinct lines, ensuring that each attribute has its own line. ls-type:: annotation hl-page:: 7 hl-color:: green id:: 64e24814-5936-4f70-90e2-4a3b525ab94a
• [:span] ls-type:: annotation hl-page:: 4 hl-color:: green id:: 64e3388f-348f-4c5c-a990-ac5056d94e21 hl-type:: area hl-stamp:: 1692612748932
• Finally, the last columns in Table 2 summarize how the optimized grammar compares to the target grammar. The results confirm that the grammar rules in the generated grammars of EAST-ADL, Bibtex, and Xenia have been optimized to be identical to the target grammar using the extracted optimization rule configurations. I ls-type:: annotation hl-page:: 8 hl-color:: yellow id:: 64e37cd9-fbab-48b1-8d2a-1ce73c5d521d
• Grammar Comparison ls-type:: annotation hl-page:: 9 hl-color:: yellow id:: 64e37cfa-dc61-4bfc-a2a9-23332b9b5b63